Complementary diffusion tensor imaging study of the corpus callosum in patients with first-episode and chronic schizophrenia
Bibliographic record
Abstract
BACKGROUND: Abnormalities in the corpus callosum have long been implicated in schizophrenia. Previous diffusion tensor imaging (DTI) studies in patients with different durations of schizophrenia yielded inconsistent results. By comparing patients with different durations of schizophrenia, we investigated if white matter abnormalities of the corpus callosum emerge at an early stage in the illness or result from pathological progression. METHODS: We recruited patients with first-episode schizophrenia, patients with chronic schizophrenia and age-, sex- and handedness-matched healthy controls. We used 2 DTI techniques (voxel-based and fibre-tracking DTI) to investigate differences in corpus callosum integrity among the 3 groups. RESULTS: With both DTI techniques, significantly decreased fractional anisotropy values were identified in the genu of corpus callosum in patients with chronic schizophrenia, but not first-episode schizophrenia, compared with healthy controls. LIMITATIONS: This study was cross-sectional, and the sample size was relatively small. CONCLUSION: Abnormalities in the genu of the corpus callosum might be a progressive process in schizophrenia, perhaps related to disease severity and prognosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".